Unit DAI401: Introduction to Artificial Intelligence and Applications
Unit code: R/651/0599
RQF Level: 4
Unit Aim:
This unit will provide students with a fundamental understanding of Artificial Intelligence
(AI) and is an introductory unit for the Diploma in Artificial Intelligence Application. Students
will gain knowledge of the evolving field of AI and explore the basic theoretical foundation
of AI as it is applied in industry.

Indicative Content
– AI principles and foundation, its technologies and impacts on society
– Trending AI application and their benefits in industries such as Education, Marketing
   and Small Businesses
– AI models and its purposes (Neural network, supervised, unsupervised, reinforced
   learning)
– AI technologies including data science, machine learning, natural language processing,
   computer vision, speech/image recognition and robotics
– Implementation of AI and its challenges.

Unit code: M/651/0605
RQF Level: 5
Unit Aim
This unit aims to provide students with fundamental knowledge of and hands-on practical
skills in performing data visualization using graphic user-interface design and effective visual

communication. Learners will cover the principles and theories of visualization and
appreciate the purpose and benefits of the different types of data visualization and their
respective tools and technologies. Students will practise applying user-experience design
principles and communication skills using popular visualization tools and software to create
interactive visualization on given datasets.

Unit code: R/651/0606
RQF level: 5
Unit Aim
This unit provides students with fundamental understanding of the principles, basic
algorithms and practical usage of reinforced machine learning. Students will be introduced
to various reinforcement machine learning algorithms, libraries and frameworks typically
used to solve business problems. Students will work a with variety of datasets to produce
simple designs and implement and evaluate reinforcement learning algorithms. 

Unit code: T/651/0607
RQF level: 5
Unit Aim
In this unit students will develop fundamental knowledge of skills in Natural Language
Processing (NLP). Students will cover the fundamental concepts and algorithms commonly
used for NLP. They will use Python libraries for NLP to build a search algorithm for extracting
information from raw text. Students will have the opportunity to perform Sentiment
Analysis to predict stakeholder sentiment. 

Unit code: Y/651/0608
RQF level: 5
Unit Aim
This unit aims to provide students with an understanding of the multidisciplinary areas
involved in the field of Human-AI interactions. While the focus is on human interaction with
AI, this unit covers the fundamental knowledge of and skills in designing an intuitive userfriendly human-machine interface. Students will consider human factors and basic elements in the psychology of human and AI interaction when using NLP interaction to influence conservational interface. Students will explore the fundamental concepts of explainable AI techniques to present transparent AI models.

Unit code: A/651/0609
RQF level: 5
Unit Aim
This unit builds on the fundamentals of deep learning, data sciences, machine learning and
Neural networks covered in the Level 4-unit Introduction to Deep Learning (AID405).
Students will explore advanced Neural network architecture and techniques and their use in
improving the performance of deep learning models. Students will have the opportunity to
use deep learning models within NLP and Reinforcement Learning on given real-life business
scenarios. 

Unit code: H/651/0610
RQF level: 5
Unit Aim
This unit focuses on the fundamentals of computer vision principles and techniques and
their applications. In addition to understanding the theoretical concepts, students will have
the opportunity to use common computer vision algorithms and systems. Students will
explore techniques for image processing, feature extraction and representation as well as
use algorithms to detect and classify objects within a given image 

• Unit AID501: Visualization
  Formative Assessment:
• Employ visualization tools to present data visualization for a given dataset
using tools like Tableau or PowerBI, focusing on clarity and effectiveness of
communication.
Summative Assessment:
• Present visualizations using appropriate tools on given real-life business
  scenario to interpret business insight
• Unit AID502: Reinforced Machine Learning
Formative Assessment:
• Practice using reinforcement machine learning algorithms, libraries and
  frameworks to solve practical business problems
Summative Assessment:
• Employ appropriate evaluation metrics in reinforcement machine learning to
   analyse performance effectiveness on given real-life problems
• Unit AID503: Natural Language Processing
Formative Assessment:
• Practice using various NLP libraries, frameworks and tools on a sample text
  representation scenario
Summative Assessment:
• Use learnt NLP techniques to examine stakeholders’ views within a chosen
real-life business scenario.
• Unit AID504: Human-AI Interaction
Formative Assessment:
• Practice the various multimodal interaction techniques to analyse the
effectiveness of different modalities for given business scenarios.
Summative Assessment:
• Investigate effective design principles for collaborative human and AI
systems.
• Unit AID505: Advanced Deep Learning
Formative Assessment:
• Practice different advance deep learning techniques to review effects on
image generations and data synthesis for given business scenarios.
Summative Assessment:
• Employ deep learning models on NLP and RML for a given real-life business
scenario.
• Unit AID506: Introduction to Computer Vision
QUALIFI Level 4 and Level 5 Diplomas in AI March 2024 Page 39 of 40
Formative Assessment:
• Practice using different techniques for image processing and feature
representations for given business scenarios.
Summative Assessment:
• Employ deep learning models in computer vision to classify images within
   given real-life business scenario

– Ethical implications of AI technologies and AI ethical framework
– Privacy, fairness, transparency, and accountability.
– Challenges of bias AI algorithms and its implication on fairness
– Methods to examine and mitigate ethical issues in AI systems
– Existing regulatory landscape in governing AI systems

All unit grading is shown on the qualification transcript.
QUALIFI Level 4 Artificial Intelligence
Pass mark is 40% for each unit.
Pass mark is 40% fo reach unit.
Fail – 0-39%
Pass – 40%-59%
Merit – 60% – 69%
Distinction 70%+

Learners’ assessments will be marked internally by the approved centre and will be subject to external moderation by QUALIFI prior to certification. 

• Visualisation
• Reinforce Machine Learning
• Natural Language Processing
• Human-AI Interaction
• Advanced Deep Machine Learning
• Introduction to Computer Vision

External Quality Assurance Arrangements All centres are required to complete an approval process to be recognised as an approved centre. Centres must have the ability to support learners. Centres must commit to working with QUALIFI and its team of External Quality Assurers (EQAs). Approved centres are required to have in place qualified and experienced tutors.

All tutors are required to undertake regular continued professional development (CPD). Approved centres will be monitored by QUALIFI External Quality Assurers (EQAs) to ensure compliance with QUALIFI requirements and to ensure that learners are provided with appropriate learning opportunities, guidance and formative assessment. QUALIFI’s guidance relating to invigilation, preventing plagiarism and collusion will apply to centres. Unless otherwise agreed, QUALIFI:

• sets all assessments.
• moderate’s assessments prior to certification.
• awards the final mark and issues certificates.

Formative Assessment

Formative assessment is an integral part of the assessment process, involving both the tutor/assessor and the learner about their progress during the course of study. Formative assessment takes place prior to summative assessment and focuses on helping the learner to reflect on their learning and improve their performance and does not confirm achievement of grades at this stage.

Summative Assessment

Summative assessment is used to evaluate learner competence and progression at the end of a unit or component. Summative assessment should take place when the assessor deems that the learner is at a stage where competence can be demonstrated. Learners should be made aware that summative assessment outcomes are subject to confirmation by the Internal Verifier and External Quality Assurer (EQA) and thus is provisional and can be overridden. Assessors should annotate on the learner work where the evidence supports their decisions against the assessment criteria. Learners will need to be familiar with the assessment and grading criteria so that they can understand the quality of what is required.

1. Deep Understanding of Advanced AI Concepts: Graduates will demonstrate an in-depth
   comprehension of advanced artificial intelligence concepts, extending their knowledge beyond the fundamentals to encompass cutting-edge theories, techniques, and principles in AI development.
2. Advanced Machine Learning Proficiency: Students will apply advanced machine learning algorithms and methodologies to tackle complex, multifaceted real-world problems, showcasing their ability to design and implement highly effective AI solutions.
3. Advanced Data Handling Skills: Graduates will possess advanced data analysis and preprocessing skills, including advanced data cleaning, transformation, and feature engineering techniques, enabling them to work with diverse and complex datasets in AI applications.
4. Proficiency in Advanced AI Programming: Students will master advanced AI programming languages and libraries, expanding their coding expertise to develop intricate AI solutions and incorporate advanced algorithms effectively.
5. Advanced Ethical and Legal Assessment: Graduates will conduct in-depth assessments of the ethical and legal implications of AI technologies, with the ability to navigate complex ethical dilemmas and compliance issues in AI development and deployment.
6. Proficient in strategic impact of AI: Students will critically evaluate the broader societal, economic, and industry impacts of AI, and they will develop the strategic acumen to propose innovative AI strategies and applications that positively influence these domains.

Units

Mandatory

• Visualisation
• Reinforce Machine Learning
• Natural Language Processing
• Human-AI Interaction
• Advanced Deep Machine Learning
• Introduction to Computer Vision

provide career path support to learners who wish to develop their management skills, enterprise capabilities and opportunities in their chosen sector

• improve learner understanding of any given business environments and organisations and how they are managed and developed
• develop skills and abilities in learners to support their professional development.

On completion of this course students have the opportunity to complete an Degree programme from a range of UK universities. With level diploma, you are qualifying for university year 3 for degree.

University of Gloucestershire
Anglia Ruskin University
University of Bolton
University of Sunderland
Westcliff University
Northampton University
University of Derby

And More:

To enrol onto the level 4 programme, you must be either.

a)a university graduate who is over 18 years old, or  
b)a non-university graduate over 24-year-old, and with at least five years of managerial experience.

All course material, including online modules and written assignments.
Personal tutor support with 1-2-1 Zoom sessions
Dedicated student support
Access to an online social learning forum
Assignment marking and feedback.
FREE TOTUM student discount card
FREE laptop*
FREE access to Our Hubs  

The fee for the level 4 Diploma in Artificial Intelligence course is £3,500. 
Students can make payment using one of the following methods:

• Credit or debit card.
• Bank transfer.
• Interest free monthly instalments
• PayPal